Abstract
The segregation of solute atoms at grain boundaries (GBs) can profoundly impact the structural properties of metallic alloys, and induce effects that range from strengthening to embrittlement. And, though known to be anisotropic, there is a limited understanding of the variation of solute segregation tendencies across the full, multidimensional GB space, which is critically important in polycrystals where much of that space is represented. Here we develop a machine learning framework that can accurately predict the segregation tendency—quantified by the segregation enthalpy spectrum—of solute atoms at GB sites in polycrystals, based solely on the undecorated (pre-segregation) local atomic environment of such sites. We proceed to use the learning framework to scan across the alloy space, and build an extensive database of segregation energy spectra for more than 250 metal-based binary alloys. The resulting machine learning models and segregation database are key to unlocking the full potential of GB segregation as an alloy design tool, and enable the design of microstructures that maximize the useful impacts of segregation.
Highlights
The segregation of solute atoms at grain boundaries (GBs) can profoundly impact the structural properties of metallic alloys, and induce effects that range from strengthening to embrittlement
The drive for a solute atom to segregate to a GB site-type (i) is quantified by the segregation enthalpy ΔHiseg, which, in solids[17], is equivalent to the internal energy difference between the solute atom occupying the GB site, and a bulk site, ΔHiseg % ΔEiseg 1⁄4 Egsobl;uite À Ecsolute; a negative ΔEiseg promotes segregation and vice versa
We use the accelerated approach to scan across the alloy space, and build an extensive database giving GB segregation spectra for all aluminum, magnesium, and transition metals-based binary alloys for which an interatomic potential exists in the Interatomic Potentials Repository[21,22] of the National Institute of Standards and Technology (NIST) - a total of 259 binary alloys
Summary
The segregation of solute atoms at grain boundaries (GBs) can profoundly impact the structural properties of metallic alloys, and induce effects that range from strengthening to embrittlement. A (50 nm)[3] Al polycrystal with an average grain size of 10 nm has roughly one million GB sites, which translates to a million atomistic calculations, where a solute atom is placed substitutionally at each GB site independently and allowed to relax This makes the task of investigating different microstructures (i.e. multiple polycrystalline samples) cost-prohibitive for a given alloy. We use the accelerated approach to scan across the alloy space, and build an extensive database giving GB segregation spectra for all aluminum, magnesium, and transition metals-based binary alloys for which an interatomic potential exists in the Interatomic Potentials Repository[21,22] of the National Institute of Standards and Technology (NIST) - a total of 259 binary alloys This database allows us to identify alloys of interest with minimal computational cost, for which high-fidelity models can be trained and used. The proposed ML framework and the resulting spectral segregation database should provide a general and broadly applicable alloy design toolbox relevant to all material properties impacted by solute segregation
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